MapReduce簡介

1. MapReduce是一種分布式計算模型,主要解決海量數據的計算問題。
2. MR有兩個階段組成：Map和Reduce，用戶只需實現map()和reduce()兩個函數，即可實現分布式計算。

MapReduce的原理圖

1. MR執行的流程

2.MR原理圖

• 根據代碼簡單了解MR。

package com.lj.MR;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;

public class WCMapper extends Mapper<LongWritable, Text, Text, IntWritable>  {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //super.map(key, value, context);
        String[] arr = value.toString().split(" ");
        Text keyOut = new Text();
        IntWritable valueOut = new IntWritable();
        for(String s :arr){
            keyOut.set(s);
            valueOut.set(1);
            try {
                context.write(keyOut,valueOut);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
        }
    }
}

package com.lj.MR;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.io.Text;

import java.io.IOException;

public class WCReducce extends Reducer<Text, IntWritable, Text, IntWritable> {
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws
 
 IOException, InterruptedException {
        //super.reduce(key, values, context);
        int count = 0;
        for(IntWritable iw:values){
             count = count + iw.get();
        }
        context.write(key,new IntWritable(count));
    }
}

package com.lj.MR;

import org.apache.hadoop.conf.Configuration;
 
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.log4j.BasicConfigurator;


public class WCApp {
    public static void main(String[] args) {
        BasicConfigurator.configure();

        Configuration conf = new Configuration();
        //此處為本地測試
        // conf.set("fs.defaultFS","file：///D://ItTools");
        try {
            //單例模式
            Job job = Job.getInstance(conf);
            //任務作業名字
            job.setJobName("WCApp");
            //搜索類
            job.setJarByClass(WCApp.class);
            //設置輸入格式
            job.setInputFormatClass(TextInputFormat.class);


            FileInputFormat.addInputPath(job, new Path(args[0]));
            FileOutputFormat.setOutputPath(job, new Path(args[1]));


            job.setMapperClass(WCMapper.class);
            job.setReducerClass(WCReducce.class);


            job.setNumReduceTasks(1);
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(IntWritable.class);

            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(IntWritable.class);
            job.waitForCompletion(false);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

• 代碼簡單解析：

根據執行流程圖我們不難發現，首先我們從Mapper下手，然後著手Reducer,而Reducer的key(in),value(in)，肯定是Mapper的key(out),value(out)，否則我們不難發現，一定會類型不匹配，直接報錯。

MAP：就是將原本文字轉換成（k,v），其中k就是word，v就是單詞的出現的次數

Shuffle：將相同的k排列一起

Reduce：將相同的k的v相加

004簡單介紹WordCount，統計文本單詞次數